Machine Learning

The course introduces machine learning through a global presentation of the field, before dealing more precisely with theoretical aspects of statistical learning and several algorithms. The practical work presents situations where the implementation of methods requires an understanding of the theory associated with them.

- Understand the theoretical foundations of the machine learning methods presented.
- Implement these methods appropriately, without considering them as black boxes.
- Make the link between the different methods.

**Document**

The document associated to this course (and others) is available online :

You may also be interested in refreshing your knowledge about probabilities. We provide you with a short reminder (in french).

**Lecture materials**

- Introduction/Bayes
- Support Vector Machines (SVM)
- Vector Quantization (VQ)
- Dimensionality reduction :

**Labworks**

All of them are here.

**Previous exams**

You can access the previous exams by following the links 2010fr, 2011fr, 2012fr, 2013en, 2014en, 2015en, 2016fr, 2017fr, 2018fr, 2019fr,